Generative adversarial networks for generation and classification of physical rehabilitation movement episodes
This work addresses the need for automated analysis and synthesis of rehabilitation movements for patients and therapists, but it is incremental as it applies existing GAN methods to a new domain-specific dataset.
The authors tackled the problem of modeling physical therapy movements by using generative adversarial networks (GANs) to classify and generate human motion sequences, achieving results that demonstrate the networks' ability to classify new motions and generate realistic motion examples from optical motion tracker data.
This article proposes a method for mathematical modeling of human movements related to patient exercise episodes performed during physical therapy sessions by using artificial neural networks. The generative adversarial network structure is adopted, whereby a discriminative and a generative model are trained concurrently in an adversarial manner. Different network architectures are examined, with the discriminative and generative models structured as deep subnetworks of hidden layers comprised of convolutional or recurrent computational units. The models are validated on a data set of human movements recorded with an optical motion tracker. The results demonstrate an ability of the networks for classification of new instances of motions, and for generation of motion examples that resemble the recorded motion sequences.